LMI Approximations for Cones of Positive Semidefinite Forms

An interesting recent trend in optimization is the application of semidefinite programming techniques to new classes of optimization problems. In particular, this trend has been successful in showing that under suitable circumstances, polynomial optimization problems can be approximated via a sequence of semidefinite programs. Similar ideas apply to conic optimization over the cone of copositive matrices and to certain optimization problems involving random variables with some known moment information. We bring together several of these approximation results by studying the approximability of cones of positive semidefinite forms (homogeneous polynomials). Our approach enables us to extend the existing methodology to new approximation schemes. In particular, we derive a novel approximation to the cone of copositive forms, that is, the cone of forms that are positive semidefinite over the nonnegative orthant. The format of our construction can be extended to forms that are positive semidefinite over more general conic domains. We also construct polyhedral approximations to cones of positive semidefinite forms over a polyhedral domain. This opens the possibility of using linear programming technology in optimization problems over these cones.

[1]  N. Z. Shor,et al.  Modifiedr-algorithm to find the global minimum of polynomial functions , 1997 .

[2]  J. Hiriart-Urruty,et al.  Convex analysis and minimization algorithms , 1993 .

[3]  D. Hilbert Über die Darstellung definiter Formen als Summe von Formenquadraten , 1888 .

[4]  Ioana Popescu,et al.  A Semidefinite Programming Approach to Optimal-Moment Bounds for Convex Classes of Distributions , 2005, Math. Oper. Res..

[5]  Ioana Popescu,et al.  Optimal Inequalities in Probability Theory: A Convex Optimization Approach , 2005, SIAM J. Optim..

[7]  Jean B. Lasserre,et al.  Global Optimization with Polynomials and the Problem of Moments , 2000, SIAM J. Optim..

[8]  P. Parrilo Structured semidefinite programs and semialgebraic geometry methods in robustness and optimization , 2000 .

[9]  B. Reznick,et al.  A new bound for Pólya's Theorem with applications to polynomials positive on polyhedra , 2001 .

[10]  Bernard Hanzon,et al.  REPORT RAPPORT , 2001 .

[11]  D. Handelman Representing polynomials by positive linear functions on compact convex polyhedra. , 1988 .

[12]  R. Jackson Inequalities , 2007, Algebra for Parents.

[13]  Y. Nesterov Structure of non-negative polynomials and optimization problems , 1997 .

[14]  Javier Peña,et al.  A Conic Programming Approach to Generalized Tchebycheff Inequalities , 2005, Math. Oper. Res..

[15]  R. Tyrrell Rockafellar,et al.  Convex Analysis , 1970, Princeton Landmarks in Mathematics and Physics.

[16]  N. Z. Shor Class of global minimum bounds of polynomial functions , 1987 .

[17]  Etienne de Klerk,et al.  Solving Standard Quadratic Optimization Problems via Linear, Semidefinite and Copositive Programming , 2002, J. Glob. Optim..

[18]  D. Hilbert Mathematical Problems , 2019, Mathematics: People · Problems · Results.

[19]  Monique Laurent,et al.  A Comparison of the Sherali-Adams, Lovász-Schrijver, and Lasserre Relaxations for 0-1 Programming , 2003, Math. Oper. Res..

[20]  Gene H. Golub,et al.  Matrix computations , 1983 .

[21]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[22]  J. Lasserre Bounds on measures satisfying moment conditions , 2002 .

[23]  Charles N. Delzell,et al.  Positive Polynomials: From Hilbert’s 17th Problem to Real Algebra , 2001 .

[24]  Katta G. Murty,et al.  Some NP-complete problems in quadratic and nonlinear programming , 1987, Math. Program..

[25]  R. Saigal,et al.  Handbook of semidefinite programming : theory, algorithms, and applications , 2000 .

[26]  I. Popescu,et al.  A SEMIDEFINITE PROGRAMMING APPROACH TO OPTIMAL MOMENT BOUNDS FOR DISTRIBUTIONS WITH CONVEX PROPERTIES , 2002 .

[27]  Sunyoung Kim,et al.  A General Framework for Convex Relaxation of Polynomial Optimization Problems over Cones , 2003 .

[28]  Monique Laurent,et al.  Semidefinite representations for finite varieties , 2007, Math. Program..

[29]  Marie-Françoise Roy,et al.  Real algebraic geometry , 1992 .

[30]  W. J. Studden,et al.  Tchebycheff Systems: With Applications in Analysis and Statistics. , 1967 .

[31]  K. Schmüdgen TheK-moment problem for compact semi-algebraic sets , 1991 .

[32]  Monique Laurent,et al.  Converging Semidefinite Bounds for Global Unconstrained Polynomial Optimization , 2007 .

[33]  D. Jibetean,et al.  Algebraic Optimization with Applications in System Theory , 2003 .

[34]  Pablo A. Parrilo,et al.  Semidefinite programming relaxations for semialgebraic problems , 2003, Math. Program..

[35]  B. Reznick Some concrete aspects of Hilbert's 17th Problem , 2000 .

[36]  Etienne de Klerk,et al.  Approximation of the Stability Number of a Graph via Copositive Programming , 2002, SIAM J. Optim..

[37]  Olga Taussky-Todd SOME CONCRETE ASPECTS OF HILBERT'S 17TH PROBLEM , 1996 .

[38]  B. Reznick Uniform denominators in Hilbert's seventeenth problem , 1995 .

[39]  T. Wörmann,et al.  Positive polynomials on compact sets , 2001 .

[40]  K. Schmüdgen TheK-moment problem for compact semi-algebraic sets , 1991 .

[41]  Etienne de Klerk,et al.  Global optimization of rational functions: a semidefinite programming approach , 2006, Math. Program..

[42]  James Renegar,et al.  A mathematical view of interior-point methods in convex optimization , 2001, MPS-SIAM series on optimization.

[43]  Gene H. Golub,et al.  Matrix Computations, Third Edition , 1996 .

[44]  P. H. Diananda On non-negative forms in real variables some or all of which are non-negative , 1962, Mathematical Proceedings of the Cambridge Philosophical Society.